GIS Shapefile, Tree Canopy Change 2007 - 2015 - Baltimore City

This layer is a high-resolution tree canopy change-detection layer for Baltimore City, MD. It contains three tree-canopy classes for the period 2007-2015: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2007 and 2015 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2007 and 2015 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction. 2006 LiDAR and 2014 LiDAR data was also used to assist in tree canopy change.

本图层为美国马里兰州巴尔的摩市的高分辨率树冠变化检测图层。该数据集涵盖2007年至2015年期间的三类树冠类别：(1) 无变化；(2) 新增；(3) 消失。本数据集通过从2007年与2015年的现有高分辨率土地覆盖图中提取树冠信息，再对提取的树冠进行直接对比而生成。两个时段均存在的树冠被归入无变化类别；因开发、风暴或病害被移除的树冠则被归入消失类别。在此间隔期内新种植的树木，以及现有树冠出现显著扩张的边缘区域，均被归入新增类别。
之所以能够进行直接对比，是因为2007年与2015年的土地覆盖图均采用面向对象影像分析（object-based image analysis, OBIA）方法生成，且使用了相似的源数据集：激光雷达（LiDAR）生成的地表模型、多光谱影像以及专题地理信息系统（GIS）输入数据。面向对象影像分析系统的工作原理为：基于像素的光谱与空间属性将其聚类为具有语义意义的对象，同时兼顾现有矢量数据集所设定的边界。在面向对象影像分析的环境中，开发了一套基于规则的专家系统，通过将影像解译要素（色彩/色调、纹理、图案、位置、尺寸与形状）融入分类流程，有效模拟了人工影像分析的过程。本研究采用了一系列形态学处理流程，以确保最终产物兼具准确性与制图美观性。
本数据集未开展精度评估，但后续将进行人工审核与修正。此外还利用2006年与2014年的激光雷达（LiDAR）数据辅助树冠变化检测分析。